Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events
Abstract The significant emergence of women’s football has stimulated considerable scientific interest, particularly in enhancing performance and achieving success. Football’s dynamic nature with its complex interactions and contextual variables, significantly influences player performance that can...
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-08-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05490-8 |
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| author | Aratz Olaizola Ibai Errekagorri Elsa Fernández Julen Castellano John Suckling Karmele Lopez-de-Ipina |
| author_facet | Aratz Olaizola Ibai Errekagorri Elsa Fernández Julen Castellano John Suckling Karmele Lopez-de-Ipina |
| author_sort | Aratz Olaizola |
| collection | DOAJ |
| description | Abstract The significant emergence of women’s football has stimulated considerable scientific interest, particularly in enhancing performance and achieving success. Football’s dynamic nature with its complex interactions and contextual variables, significantly influences player performance that can affect match outcomes. While goals are vital for securing a win, they can also trigger unexpected psychological responses such as stress and pressure potentially altering player behaviour and impacting the match’s trajectory. Effectively predicting and managing these behavioural shifts is important to in-game regulation. This study aims to enhance the performance and in-game success in women’s football by developing machine learning (ML) models that predict match outcomes based on player and team behaviour following goals. We applied a comprehensive approach that integrates spatiotemporal and behavioural data during the transitional period following goals focusing on team dynamics, including chaotic and collective behavioural analysis with entropy and fractality, spatial area, movement trajectories, and locomotor patterns. Several well-established ML models and feature extraction techniques were deployed with overall good performance of greater than 70% accuracy, with some specific methodology combinations have superior performance. Self-reported player wellness did not contribute to the predictions. In conclusion, game outcomes can be predicted with reasonable accuracy based on player behaviour during a relatively small proportion of game time, although this time represents events of high stress and pressure. |
| format | Article |
| id | doaj-art-ccd2fd688a1b4b2ca419fa77f5b1d0df |
| institution | Kabale University |
| issn | 2662-9992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj-art-ccd2fd688a1b4b2ca419fa77f5b1d0df2025-08-20T03:42:48ZengSpringer NatureHumanities & Social Sciences Communications2662-99922025-08-0112111010.1057/s41599-025-05490-8Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress eventsAratz Olaizola0Ibai Errekagorri1Elsa Fernández2Julen Castellano3John Suckling4Karmele Lopez-de-Ipina5Department of Physical Education and Sport, University of the Basque Country (UPV/EHU)Department of Physical Education and Sport, University of the Basque Country (UPV/EHU)Department of Computational Science and Artificial Intelligence, University of the Basque Country (UPV/EHU)Department of Physical Education and Sport, University of the Basque Country (UPV/EHU)Department of Psychiatry, University of CambridgeDepartment of Psychiatry, University of CambridgeAbstract The significant emergence of women’s football has stimulated considerable scientific interest, particularly in enhancing performance and achieving success. Football’s dynamic nature with its complex interactions and contextual variables, significantly influences player performance that can affect match outcomes. While goals are vital for securing a win, they can also trigger unexpected psychological responses such as stress and pressure potentially altering player behaviour and impacting the match’s trajectory. Effectively predicting and managing these behavioural shifts is important to in-game regulation. This study aims to enhance the performance and in-game success in women’s football by developing machine learning (ML) models that predict match outcomes based on player and team behaviour following goals. We applied a comprehensive approach that integrates spatiotemporal and behavioural data during the transitional period following goals focusing on team dynamics, including chaotic and collective behavioural analysis with entropy and fractality, spatial area, movement trajectories, and locomotor patterns. Several well-established ML models and feature extraction techniques were deployed with overall good performance of greater than 70% accuracy, with some specific methodology combinations have superior performance. Self-reported player wellness did not contribute to the predictions. In conclusion, game outcomes can be predicted with reasonable accuracy based on player behaviour during a relatively small proportion of game time, although this time represents events of high stress and pressure.https://doi.org/10.1057/s41599-025-05490-8 |
| spellingShingle | Aratz Olaizola Ibai Errekagorri Elsa Fernández Julen Castellano John Suckling Karmele Lopez-de-Ipina Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events Humanities & Social Sciences Communications |
| title | Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events |
| title_full | Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events |
| title_fullStr | Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events |
| title_full_unstemmed | Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events |
| title_short | Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events |
| title_sort | predicting female football outcomes by machine learning behavioural analysis of goals as high stress events |
| url | https://doi.org/10.1057/s41599-025-05490-8 |
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